A face detection technique is one of the fields being researched in many application systems such as moving robots, surveillance systems, interactions between human and robot, and command input using head tracking. Even though it is difficult to detect a face in real time using a current vision technique, this is being actively researched, and progress is being made in image processing techniques due to the development of computer performance.
In particular, since an algorithm for searching a location of a template image from an inputted image has been developed, as disclosed in “An Iterative Image Registration Technique with an Application to Stereo Vision” {Bruce D. Lucas and Takeo Kanade, Proceedings of the 7th International Joint Conference on Artificial Intelligence (IJCAI '81), April, 1981, pp. 674-679}, algorithms for tracking the rotation angle of a face from a 2D image are progressing in various ways.
For the above algorithms, methods such as an active contour model (ACM), an active shape model (ASM), and an active appearance model (AAM) are being employed.
The ACM is also referred to as a snake model because the shape of its searching is similar to the motion of a snake. This model is a deformable model that can track a contour of an object. The algorithm utilized by the ACM model is a non-learning type, and while it can search respective points relatively rapidly using the Euler formula, it is difficult to have confidence in the results due to convergences by image terms and constraint terms, and it is difficult to track the contour at a concavity portion.
The ASM is a technique proposed by Cootes et al. and has characteristics similar to the ACM model. A shape model refers to all geometric information of a certain object's shape, where the location, size, and rotation effects are removed. The ASM that uses this shape model is a method for learning the information of an object appearance, modifying the average shape of the object based on this information, and thereby searching the object in a new image. This method is based on pre-learned knowledge, and therefore the searching is performed while maintaining the shape of the object. Although this method has rapid operation speed, the portions forming the appearance of an object must be searched accurately when searching the object in a new image, and thus it is difficult to search the accurate shape of an object when an image has unclear contours, similarly to the ACM.
The AAM, wherein the ASM technique is improved, is a method for searching an object in an image by using an appearance, which includes object shape information of a conventional ASM and texture information of the object. This method allows for searching the shape and location of an object more accurately by searching a portion that has the most similar texture, based on pre-learned texture information, while maintaining the shape of the object, and using the shape information and texture information of the object.
More specifically, the AAM technique performs an operation for completing the appearance based on the texture information. Concurrently, shape information is collected through the extraction of feature points, and based on this information, an initial shape model is generated. It is possible to organize the AAM model by using the initial shape model and the appearance.
The AAM may be classified into a combined AAM and an independent AAM. The combined AAM algorithm is a learning type and is a technique where the fitting is performed by parameterizing a shape and an appearance as one parameter; however, it has a relatively low fitting speed. The independent AAM algorithm is a learning type, and a shape and an appearance are independent; thus, the length of a vector is prolonged to infinity, and its detection rate is advantageous when compared to the combined AAM.
Such AAM is described in detail in “Active Appearance Models Revisited” Jain Matthews and Simon Baker, International Journal of Computer Vision, Vol. 60, No. 2, November, 2004, pp. 135-164). As explained above, the AAM has a slow operation speed, and thus, it cannot track in real time a facial area which moves as rotating to the left and right quickly (i.e., the movement of a head).
To solve the problem above, a facial area tracking technique using a dynamic template has been developed as disclosed in “Real-Time 3D Head Tracking Under Rapidly Changing Pose, Head Movement and Illumination” (Wooju Ryu and Daijin Kim, Lecture Notes in Computer Science, 2007, Volume 4633/2007, pp. 569-580).
The facial area tracking technique using the dynamic template searches the movement of a facial area (i.e., the movement of a head) by using as a template, an image that was just previously inputted among consecutively inputted images, and comparing differences between the template and the current image. Such a facial area tracking technique using a dynamic template is appropriate for tracking in real time a facial area that is moving to the left and right quickly because the image used as a template is updated in real time. However, once the tracking result has become erroneous, it cannot be restored, and since a facial area is tracked by using an inputted image as a template, error is accumulated for each image that is consecutively inputted, thereby reducing accuracy. In addition, a user has to manually set an initial template when tracking a facial area, and therefore, automatic initialization is restricted.